Post-transcriptional regulation across human tissues
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Abstract
Transcriptional and post-transcriptional regulation shape tissue-type-specific proteomes, but their relative contributions remain contested. Estimates of the factors determining protein levels in human tissues do not distinguish between (i) the factors determining the variability between the abundances of different proteins, i.e., mean-level-variability and, (ii) the factors determining the physiological variability of the same protein across different tissue types, i.e., across-tissues variability. We sought to estimate the contribution of transcript levels to these two orthogonal sources of variability, and found that scaled mRNA levels can account for most of the mean-level-variability but not necessarily for across-tissues variability. The reliable quantification of the latter estimate is limited by substantial measurement noise. However, protein-to-mRNA ratios exhibit substantial across-tissues variability that is functionally concerted and reproducible across different datasets, suggesting extensive post-transcriptional regulation. These results caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation to shaping tissue-type-specific proteomes.
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- Authors
- Franks, A., Airoldi, E., & Slavov, N.
- Affiliations
- University of Washington (US)
- Broad Institute (US)
- Northeastern University (US)
- Corresponding author
- Franks, Alexander
- Airoldi, Edoardo
- Slavov, Nikolai
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-05-08
- Volume
- 13
- Issue
- 5
- Pages
- e1005535
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5440056
- OpenAlex
- W2951242120
- Cited by
- 219
- References
- 10
- Citations by year
- 2026: 33 · 2025: 20 · 2024: 19 · 2023: 25 · 2022: 25 · 2021: 28 · 2020: 24 · 2019: 23 · 2018: 21 · 2017: 1
- FWCI
- 7.71
- Type
- journal article
- Language
- en
- Update notices
- new_version (2017-05-22)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Institute of General Medical Sciences (DP2-GM-123497)
- Washington Research Foundation Fund for Innovation in Data-Intensive Discovery and the Moore/Sloan Data Science Environments Project at the University of Washington
- Broad Institute (SPARC Award)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- post-transcriptional regulation, transcriptome, proteome, mRNA levels, human tissue
- MeSH terms
- Gene Expression Regulation, Humans, Organ Specificity, RNA, Messenger, Transcription, Genetic, Proteome, Databases, Protein
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